"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class   __UpperCAmelCase		(      __lowerCAmelCase , unittest.TestCase      ):
				A__ :  int    				=			UnCLIPImageVariationPipeline
				A__ :  Any    				=			IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
				A__ :  Optional[int]    				=			IMAGE_VARIATION_BATCH_PARAMS

				A__ :  Dict    				=			[
				    '''generator''',
				    '''return_dict''',
				    '''decoder_num_inference_steps''',
				    '''super_res_num_inference_steps''',
				]
				A__ :  List[str]    				=			False

				@property
				def 						_a      ( self ):
										return 32

				@property
				def 						_a      ( self ):
										return 32

				@property
				def 						_a      ( self ):
										return self.time_input_dim

				@property
				def 						_a      ( self ):
										return self.time_input_dim * 4

				@property
				def 						_a      ( self ):
										return 100

				@property
				def 						_a      ( self ):
										lowerCamelCase__   =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
										return tokenizer

				@property
				def 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =CLIPTextConfig(
										    bos_token_id=0      ,					eos_token_id=2      ,					hidden_size=self.text_embedder_hidden_size      ,					projection_dim=self.text_embedder_hidden_size      ,					intermediate_size=37      ,					layer_norm_eps=1E-05      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=1000      ,					)
										return CLIPTextModelWithProjection(_lowerCamelCase )

				@property
				def 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size      ,					projection_dim=self.text_embedder_hidden_size      ,					num_hidden_layers=5      ,					num_attention_heads=4      ,					image_size=32      ,					intermediate_size=37      ,					patch_size=1      ,					)
										return CLIPVisionModelWithProjection(_lowerCamelCase )

				@property
				def 						_a      ( self ):
										torch.manual_seed(0 )

										lowerCamelCase__   ={
										    "clip_embeddings_dim": self.text_embedder_hidden_size,
										    "time_embed_dim": self.time_embed_dim,
										    "cross_attention_dim": self.cross_attention_dim,
										}

										lowerCamelCase__   =UnCLIPTextProjModel(**_lowerCamelCase )
										return model

				@property
				def 						_a      ( self ):
										torch.manual_seed(0 )

										lowerCamelCase__   ={
										    "sample_size": 32,
										    # RGB in channels
										    "in_channels": 3,
										    # Out channels is double in channels because predicts mean and variance
										    "out_channels": 6,
										    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
										    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
										    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
										    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
										    "layers_per_block": 1,
										    "cross_attention_dim": self.cross_attention_dim,
										    "attention_head_dim": 4,
										    "resnet_time_scale_shift": "scale_shift",
										    "class_embed_type": "identity",
										}

										lowerCamelCase__   =UNetaDConditionModel(**_lowerCamelCase )
										return model

				@property
				def 						_a      ( self ):
										return {
										    "sample_size": 64,
										    "layers_per_block": 1,
										    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
										    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
										    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
										    "in_channels": 6,
										    "out_channels": 3,
										}

				@property
				def 						_a      ( self ):
										torch.manual_seed(0 )

										lowerCamelCase__   =UNetaDModel(**self.dummy_super_res_kwargs )
										return model

				@property
				def 						_a      ( self ):
										# seeded differently to get different unet than `self.dummy_super_res_first`
										torch.manual_seed(1 )

										lowerCamelCase__   =UNetaDModel(**self.dummy_super_res_kwargs )
										return model

				def 						_a      ( self ):
										lowerCamelCase__   =self.dummy_decoder
										lowerCamelCase__   =self.dummy_text_proj
										lowerCamelCase__   =self.dummy_text_encoder
										lowerCamelCase__   =self.dummy_tokenizer
										lowerCamelCase__   =self.dummy_super_res_first
										lowerCamelCase__   =self.dummy_super_res_last

										lowerCamelCase__   =UnCLIPScheduler(
										    variance_type="learned_range"      ,					prediction_type="epsilon"      ,					num_train_timesteps=1000      ,					)

										lowerCamelCase__   =UnCLIPScheduler(
										    variance_type="fixed_small_log"      ,					prediction_type="epsilon"      ,					num_train_timesteps=1000      ,					)

										lowerCamelCase__   =CLIPImageProcessor(crop_size=32      ,					size=32 )

										lowerCamelCase__   =self.dummy_image_encoder

										return {
										    "decoder": decoder,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "text_proj": text_proj,
										    "feature_extractor": feature_extractor,
										    "image_encoder": image_encoder,
										    "super_res_first": super_res_first,
										    "super_res_last": super_res_last,
										    "decoder_scheduler": decoder_scheduler,
										    "super_res_scheduler": super_res_scheduler,
										}

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase=0      ,					_lowerCamelCase=True ):
										lowerCamelCase__   =floats_tensor((1, 3, 32, 32)      ,					rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
										if str(_lowerCamelCase ).startswith("mps" ):
																lowerCamelCase__   =torch.manual_seed(_lowerCamelCase )
										else:
																lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )

										if pil_image:
																lowerCamelCase__   =input_image * 0.5 + 0.5
																lowerCamelCase__   =input_image.clamp(0      ,					1 )
																lowerCamelCase__   =input_image.cpu().permute(0      ,					2      ,					3      ,					1 ).float().numpy()
																lowerCamelCase__   =DiffusionPipeline.numpy_to_pil(_lowerCamelCase )[0]

										return {
										    "image": input_image,
										    "generator": generator,
										    "decoder_num_inference_steps": 2,
										    "super_res_num_inference_steps": 2,
										    "output_type": "np",
										}

				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"

										lowerCamelCase__   =self.get_dummy_components()

										lowerCamelCase__   =self.pipeline_class(**_lowerCamelCase )
										lowerCamelCase__   =pipe.to(_lowerCamelCase )

										pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase      ,					pil_image=_lowerCamelCase )

										lowerCamelCase__   =pipe(**_lowerCamelCase )
										lowerCamelCase__   =output.images

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase      ,					pil_image=_lowerCamelCase )

										lowerCamelCase__   =pipe(
										    **_lowerCamelCase      ,					return_dict=_lowerCamelCase      ,					)[0]

										lowerCamelCase__   =image[0, -3:, -3:, -1]
										lowerCamelCase__   =image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										lowerCamelCase__   =np.array(
										    [
										        0.9_9_9_7,
										        0.0_0_0_2,
										        0.9_9_9_7,
										        0.9_9_9_7,
										        0.9_9_6_9,
										        0.0_0_2_3,
										        0.9_9_9_7,
										        0.9_9_6_9,
										        0.9_9_7_0,
										    ] )

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2

				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"

										lowerCamelCase__   =self.get_dummy_components()

										lowerCamelCase__   =self.pipeline_class(**_lowerCamelCase )
										lowerCamelCase__   =pipe.to(_lowerCamelCase )

										pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase      ,					pil_image=_lowerCamelCase )

										lowerCamelCase__   =pipe(**_lowerCamelCase )
										lowerCamelCase__   =output.images

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase      ,					pil_image=_lowerCamelCase )

										lowerCamelCase__   =pipe(
										    **_lowerCamelCase      ,					return_dict=_lowerCamelCase      ,					)[0]

										lowerCamelCase__   =image[0, -3:, -3:, -1]
										lowerCamelCase__   =image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										lowerCamelCase__   =np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1] )

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2

				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"

										lowerCamelCase__   =self.get_dummy_components()

										lowerCamelCase__   =self.pipeline_class(**_lowerCamelCase )
										lowerCamelCase__   =pipe.to(_lowerCamelCase )

										pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase      ,					pil_image=_lowerCamelCase )
										lowerCamelCase__   =[
										    pipeline_inputs["image"],
										    pipeline_inputs["image"],
										]

										lowerCamelCase__   =pipe(**_lowerCamelCase )
										lowerCamelCase__   =output.images

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase      ,					pil_image=_lowerCamelCase )
										lowerCamelCase__   =[
										    tuple_pipeline_inputs["image"],
										    tuple_pipeline_inputs["image"],
										]

										lowerCamelCase__   =pipe(
										    **_lowerCamelCase      ,					return_dict=_lowerCamelCase      ,					)[0]

										lowerCamelCase__   =image[0, -3:, -3:, -1]
										lowerCamelCase__   =image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (2, 64, 64, 3)

										lowerCamelCase__   =np.array(
										    [
										        0.9_9_9_7,
										        0.9_9_8_9,
										        0.0_0_0_8,
										        0.0_0_2_1,
										        0.9_9_6_0,
										        0.0_0_1_8,
										        0.0_0_1_4,
										        0.0_0_0_2,
										        0.9_9_3_3,
										    ] )

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2

				def 						_a      ( self ):
										lowerCamelCase__   =torch.device("cpu" )






										class   __UpperCAmelCase		:
														A__ :  Optional[Any]    				=			1





										lowerCamelCase__   =self.get_dummy_components()

										lowerCamelCase__   =self.pipeline_class(**_lowerCamelCase )
										lowerCamelCase__   =pipe.to(_lowerCamelCase )

										pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
										lowerCamelCase__   =pipe.decoder.dtype
										lowerCamelCase__   =1

										lowerCamelCase__   =(
										    batch_size,
										    pipe.decoder.config.in_channels,
										    pipe.decoder.config.sample_size,
										    pipe.decoder.config.sample_size,
										)
										lowerCamelCase__   =pipe.prepare_latents(
										    _lowerCamelCase      ,					dtype=_lowerCamelCase      ,					device=_lowerCamelCase      ,					generator=_lowerCamelCase      ,					latents=_lowerCamelCase      ,					scheduler=DummyScheduler() )

										lowerCamelCase__   =(
										    batch_size,
										    pipe.super_res_first.config.in_channels // 2,
										    pipe.super_res_first.config.sample_size,
										    pipe.super_res_first.config.sample_size,
										)
										lowerCamelCase__   =pipe.prepare_latents(
										    _lowerCamelCase      ,					dtype=_lowerCamelCase      ,					device=_lowerCamelCase      ,					generator=_lowerCamelCase      ,					latents=_lowerCamelCase      ,					scheduler=DummyScheduler() )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase      ,					pil_image=_lowerCamelCase )

										lowerCamelCase__   =pipe(
										    **_lowerCamelCase      ,					decoder_latents=_lowerCamelCase      ,					super_res_latents=_lowerCamelCase ).images

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase      ,					pil_image=_lowerCamelCase )
										# Don't pass image, instead pass embedding
										lowerCamelCase__   =pipeline_inputs.pop("image" )
										lowerCamelCase__   =pipe.image_encoder(_lowerCamelCase ).image_embeds

										lowerCamelCase__   =pipe(
										    **_lowerCamelCase      ,					decoder_latents=_lowerCamelCase      ,					super_res_latents=_lowerCamelCase      ,					image_embeddings=_lowerCamelCase      ,					).images

										# make sure passing text embeddings manually is identical
										assert np.abs(img_out_a - img_out_a ).max() < 1E-4

				@skip_mps
				def 						_a      ( self ):
										lowerCamelCase__   =torch_device == "cpu"

										# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
										lowerCamelCase__   =1E-2

										self._test_attention_slicing_forward_pass(
										    test_max_difference=_lowerCamelCase      ,					expected_max_diff=_lowerCamelCase )

				@skip_mps
				def 						_a      ( self ):
										lowerCamelCase__   =torch_device == "cpu"
										lowerCamelCase__   =True
										lowerCamelCase__   =[
										    "decoder_num_inference_steps",
										    "super_res_num_inference_steps",
										]

										self._test_inference_batch_single_identical(
										    test_max_difference=_lowerCamelCase      ,					relax_max_difference=_lowerCamelCase      ,					additional_params_copy_to_batched_inputs=_lowerCamelCase      ,					)

				def 						_a      ( self ):
										lowerCamelCase__   =[
										    "decoder_num_inference_steps",
										    "super_res_num_inference_steps",
										]

										if torch_device == "mps":
																# TODO: MPS errors with larger batch sizes
																lowerCamelCase__   =[2, 3]
																self._test_inference_batch_consistent(
																    batch_sizes=_lowerCamelCase      ,					additional_params_copy_to_batched_inputs=_lowerCamelCase      ,					)
										else:
																self._test_inference_batch_consistent(
																    additional_params_copy_to_batched_inputs=_lowerCamelCase )

				@skip_mps
				def 						_a      ( self ):
										return super().test_dict_tuple_outputs_equivalent()

				@skip_mps
				def 						_a      ( self ):
										return super().test_save_load_local()






				@skip_mps
				def 						_a      ( self ):
										return super().test_save_load_optional_components()






@slow
@require_torch_gpu
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






				def 						_a      ( self ):
										lowerCamelCase__   =load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png" )
										lowerCamelCase__   =load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/unclip/karlo_v1_alpha_cat_variation_fp16.npy" )

										lowerCamelCase__   =UnCLIPImageVariationPipeline.from_pretrained(
										    "kakaobrain/karlo-v1-alpha-image-variations"      ,					torch_dtype=torch.floataa )
										lowerCamelCase__   =pipeline.to(_lowerCamelCase )
										pipeline.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =torch.Generator(device="cpu" ).manual_seed(0 )
										lowerCamelCase__   =pipeline(
										    _lowerCamelCase      ,					generator=_lowerCamelCase      ,					output_type="np"      ,					)

										lowerCamelCase__   =output.images[0]

										assert image.shape == (256, 256, 3)

										assert_mean_pixel_difference(_lowerCamelCase      ,					_lowerCamelCase      ,					15 )



"""simple docstring"""


from __future__ import annotations

a				='#'






class   __UpperCAmelCase		:

				def __init__( self ):
										lowerCamelCase__   ={}

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =self._trie
										for char in text:
																if char not in trie:
																						lowerCamelCase__   ={}
																lowerCamelCase__   =trie[char]
										lowerCamelCase__   =True

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =self._trie
										for char in prefix:
																if char in trie:
																						lowerCamelCase__   =trie[char]
																else:
																						return []
										return self._elements(_lowerCamelCase )






				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =[]
										for c, v in d.items():
																lowerCamelCase__   =[" "] if c == END else [(c + s) for s in self._elements(_lowerCamelCase )]
																result.extend(_lowerCamelCase )
										return tuple(_lowerCamelCase )





a				=Trie()
a				=('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
	trie.insert_word(word)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple:




						'''simple docstring'''



						lowerCamelCase__   =trie.find_word(__lowerCAmelCase							)
						return tuple(string + word for word in suffixes							)





def      lowerCamelCase_					(   )				->   None:




						'''simple docstring'''



						print(autocomplete_using_trie("de"							)							)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()


